Entropy minimization (ENT) is proposed initially in [1]} as a form of semi-supervised learning. Assuming that the model should be confident about its prediction for the unlabeled data, we can minimize the entropy of the predicted probability on the unlabeled data. We add this loss term to our method and show that it helps the learning with a good margin on standard domain adaptation datasets. Hence, we add the following loss term to the optimization in Eq. REF :
\( \mathcal {L}^t_{ent}(f) = \sum _i ent(f(x^u_i))\) 
